RETRACTED ARTICLE: Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model

Abstract

The availability of accurate solar radiation data is essential for designing as well as simulating the solar energy systems. In this study, by employing the long-term daily measured solar data, a neural network auto-regressive model with exogenous inputs (NN-ARX) is applied to predict daily horizontal global solar radiation using day of the year as the sole input. The prime aim is to provide a convenient and precise way for rapid daily global solar radiation prediction, for the stations and their immediate surroundings with such an observation, without utilizing any meteorological-based inputs. To fulfill this, seven Iranian cities with different geographical locations and solar radiation characteristics are considered as case studies. The performance of NN-ARX is compared against the adaptive neurofuzzy inference system (ANFIS). The achieved results prove that day of the year-based prediction of daily global solar radiation by both NN-ARX and ANFIS models would be highly feasible owing to the accurate predictions attained. Nevertheless, the statistical analysis indicates the superiority of NN-ARX over ANFIS. In fact, the NN-ARX model represents high potential to follow the measured data favorably for all cities. For the considered cities, the attained statistical indicators of mean absolute bias error, root mean square error, and coefficient of determination for the NN-ARX models are in the ranges of 0.44-0.61 kWh/m 2 , 0.50-0.71 kWh/m 2 , and 0.78-0.91, respectively.

Half-hour global solar radiation forecasting based on static and dynamic multivariate neural networks

“…3. The DNFC model is based on the autoregressive process with exogenous inputs (ARX), commonly used in time series modeling (Gani et al, 2016;Cardona et al, 2017;Rzadkowski et al, 2015). The output of the DNFC is given by the following equation: (12) where the next value of the dependent output signal is regressed on the previous values of the predicted output signal and independent exogenous input signal .…”

“…(2016) have applied the ANN technique for predicting global horizontal irradiation (GHI) for major locations in Zimbabwe using geographical data and some meteorological parameters. Gani et al (2016) have adopted the autoregressive ANN with exogenous inputs (NN-ARX) and the adaptive neuro-fuzzy inference system (ANFIS) in order to predict the daily horizontal global solar radiation using the day of the year as a single input. Gutierrez-Corea et al (2016) have applied the ANN technique in the short-term prediction of global solar irradiance (GSI) by using a new methodology based on different meteorological variables observations recorded in parallel by neighboring sensors.…”

Multilayer Artificial Approach for Estimating Optimal Solar PV System Power Using the MPPT Technique

“…Regarding the rapid development of solar energy in the economy of cities, few cities have the measuring equipment used for the precise measurement of various parameters. In addition, the data related to the extraction of PV power to switch to a non-polluting environment, as such predictive approaches become interesting (Timilsina, Kurdgelashvili & Narbel, 2012) The power can be estimated by applying models based on artificial intelligence (Gani et al, 2016;Li, Wang & Wei, 2018). A frequently discussed problem concerning correlation models predicting the optimum power of the SOLON 55W PV panel is the optimum choice of the nature and number of the input parameters (irradiation and temperature).…”